PI: Kristen Grauman Institution: The University of Texas at Austin
As it becomes increasingly feasible to capture, transmit, and store image and video content on a large scale, the need for machine vision algorithms capable of interpreting it is undeniable. The opportunities appear vast, but progress towards large-scale visual recognition hinges on the development of computationally efficient methods that can effectively leverage minimal supervision. The proposed research considers how informative but incomplete cues can contribute to the learning process, with the goal of enabling large volumes of visual data to be efficiently organized and queried, and a greater number of visual categories to be recognized.
This project intends to advance the scale of the recognition problem by using fragments of supervision, even when they are inexact or dynamic. The PI and her team will develop methods to allow very large image databases to be searched according to distance functions inferred from sparse similarity constraints. They will consider visual category learning scenarios where the system itself actively requests only the most useful information, and integrates ambiguous cues from external modalities such as text. As knowledge about an image collection evolves over time, so must the associated search structure. The PI will investigate ways to adapt image indexing techniques according to dynamic constraints. The proposed technical plan calls for a combination of ideas from vision, learning, and algorithms. Scalable recognition and image search will affect the extent to which visual data can be accessed and mined, making this work relevant to other scientific disciplines where images capture vital information but currently lack proper tools for large-scale analysis. The project also entails complementary educational and outreach activities aimed at engaging students in research, furthering communication across related areas, and encouraging young students to consider studying computer science or engineering.
Updates will be available from: www.cs.utexas.edu/¡Âgrauman/
